The GAP (Universidad Politécnica de Valencia, Spain) and HPCA (Universidad Jaume I, Spain) research groups are proud to announce the public release of rCUDA 1.0. The rCUDA Framework enables the concurrent usage of CUDA-compatible devices remotely by employing the sockets API for communication between clients and servers. Thus, it can be useful in three different environments:

• Clusters. To reduce the number of GPUs installed in High Performance Clusters. This leads to energy savings, as well as other related savings like acquisition costs, maintenance, space, cooling, etc.
• Academia. In low performance networks, to offer access to a few high performance GPUs concurrently to all the students.
• Virtual Machines. To enable the access to the CUDA facilities on the physical machine.

The current version of rCUDA (v1.0) implements all functions in the CUDA Runtime API version 2.3, excluding OpenGL and Direct3D interoperability. rCUDA 1.0 targets the Linux OS (for 32- and 64-bit architectures) on both client and server sides. The framework is free for any purpose under the terms and conditions of the GNU GPL/LGPL (where applicable) licenses.

For additional information, visit the rCUDA web page or Antonio Peña’s webpage.

The first GPUs to feature NVIDIA’s new Fermi architecture, the GeForce GTX 480 and 470 GPUs have 480 and 448 CUDA cores, respectively.  From an NVIDIA press release:

SANTA CLARA, California—March 29, 2010—Hot off the heels of PAX East, the consumer gaming show held this past weekend in Boston, NVIDIA today officially launched its new flagship graphics processors, the NVIDIA^® GeForce^® GTX 480 and GeForce GTX 470.

The top-of-the line in a new family of enthusiast-class GPUs, the GeForce GTX 480 was designed from the ground up to deliver the industry’s most potent tessellation performance, which is the key component of Microsoft’s DirectX 11 development platform for PC games. Tessellation allows game developers to take advantage of the GeForce GTX 480 GPU’s ability to increase the geometric complexity of models and characters to deliver far more realistic and visually compelling gaming environments.

The GeForce GTX 480 is joined by the GeForce GTX 470 as the first products in NVIDIA’s Fermi line of consumer products. They will be available in mid-April, from the world’s leading add-in card partners and PC system builders. The remainder of the GeForce 400-series lineup will be announced in the coming months, filling out additional performance and price segments.

The GeForce GTX 480 and GTX 470 GPUs bring a host of new gaming features never before offered for the PC – including support for real-time ray tracing and NVIDIA 3D Vision™ Surround for truly immersive widescreen, stereoscopic 3D gaming.

The SpeedIT Tools library provides a set of accelerated solvers for sparse linear systems of equations. Manifold acceleration, e.g. more than an order of magnitude, is achieved with a single reasonably priced NVIDIA Graphics Processing Unit (GPU) that supports CUDA and proprietary advanced optimization techniques. The library can be used in a wide spectrum of domains arising from problems with underlying 2D and 3D geometry, such as computational fluid dynamics, electro-magnetics, thermodynamics, materials, acoustics, computer vision and graphics, robotics, semiconductor devices and structural engineering. The library can be also used for problems without defined geometry such as quantum chemistry, statistics, power networks and other graphs and chemical process simulation. All computations are performed with single or double floating point precision. Two version of SpeedIT toolkit have been released: The classic version provides a conjugate gradient solver, and the extreme edition provides optimized CG, BiCGSTAB, diagonal preconditioner, memory management, and heuristic-based analysis of input matrices.

Version 1.2 of Thrust, an open-source template library for developing CUDA applications, has been released. Modeled after the C++ Standard Template Library (STL), Thrust brings a familiar abstraction layer to the realm of GPU computing. This version adds several new features, including:

• support for multicore CPUs via OpenMP
• support for CUDA 3.0 and new GPUs based on the Fermi architecture
• support for the Ocelot virtual machine
• pseudo random number generation
• key-value reduction
• set intersection
• and many more

The Thrust web page provides a quick-start guide, online documentation, many examples and introductory slides. Thrust is open-source software distributed under the OSI-approved Apache License v2.0.

Palix Technologies has introduced a new Computational Fluid Dynamics (CFD) product called ANDSolver that has been designed from the ground up to use Graphics Processing Units (GPUs) for fast and efficient aerodynamic analysis. Although developing and running applications to use multiple CPUs is a well established practice for high performance science and engineering simulations, a newer trend towards using GPUs for computation promises faster results with lower hardware acquisition and operating costs. ANDSolver delivers on that promise with up to a 10x speedup compared to a typical quad core CPU. This level of performance is unique in that it is achieved on unstructured meshes which have traditionally not been considered amenable to GPUs because of the memory access patterns. However, based on an innovative algorithm design to maximize the performance of the NVIDIA CUDA architecture, the ease and flexibility of unstructured meshing can now be used on high-performance, cost-effective GPUs.

A limited number of additional registrants will be accepted prior to our first production release in Q2 2010. More information can be found at http://www.palixtech.com for our current beta testing program.

Abstract:

We present our effort in developing an open-source GPU (graphics processing units) code library for the MATLAB Image Processing Toolbox (IPT). We ported a dozen of representative functions from IPT and based on their inherent characteristics, we grouped these functions into four categories: data independent, data sharing, algorithm dependent and data dependent. For each category, we present a detailed case study, which reveals interesting insights on how to efficiently optimize the code for GPUs and highlight performance-critical hardware features, some of which have not been well explored in existing literature. Our results show drastic speedups for the functions in the data-independent or data-sharing category by leveraging hardware support judiciously; and moderate speedups for those in the algorithm-dependent category by careful algorithm selection and parallelization. For the functions in the last category, fine-grain synchronization and data-dependency requirements are the main obstacles to an efficient implementation on GPUs.

(J. Kong, et. al., “Accelerating MATLAB Image Processing Toolbox Functions on GPUs”, Proceedings of the Third Workshop on General-Purpose Computation on Graphics Processing Units (GPGPU-3), Pittsburgh, PA. Apr. 2010. Source code is available here.)

NVIDIA has released version 3.0 of the CUDA Toolkit, providing developers with tools to prepare for the upcoming Fermi-based GPUs. Highlights of this release include:

• Support for the new Fermi architecture, with:
  • Native 64-bit GPU support
  • Multiple Copy Engine support
  • ECC reporting
  • Concurrent Kernel Execution
  • Fermi HW debugging support in cuda-gdb
  • Fermi HW profiling support for CUDA C and OpenCL in Visual Profiler
• C++ Class Inheritance and Template Inheritance support for increased programmer productivity
• A new unified interoperability API for Direct3D and OpenGL, with support for:
  • OpenGL texture interop
  • Direct3D 11 interop support
  • CUDA Driver / Runtime Buffer Interoperability, which allows applications using the CUDA Driver API to also use libraries implemented using the CUDA C Runtime such as CUFFT and CUBLAS.
• Read the rest of this entry »

Abstract:

Heterogeneous systems, systems with multiple processors tailored for specialized tasks, are challenging programming environments. While it may be possible for domain experts to optimize a high performance application for a very specific and well documented system, it may not perform as well or even function on a different system. Developers who have less experience with either the application domain or the system architecture may devote a significant effort to writing a program that merely functions correctly. We believe that a comprehensive analysis and modeling framework is necessary to ease application development and automate program optimization on heterogeneous platforms.

This paper reports on an empirical evaluation of 25 CUDA applications on four GPUs and three CPUs, leveraging the Ocelot dynamic compiler infrastructure which can execute and instrument the same CUDA applications on either target. Using a combination of instrumentation and statistical analysis, we record 37 different metrics for each application and use them to derive relationships between program behavior and performance on heterogeneous processors. These relationships are then fed into a modeling framework that attempts to predict the performance of similar classes of applications on different processors. Most significantly, this study identifies several non-intuitive relationships between program characteristics and demonstrates that it is possible to accurately model CUDA kernel performance using only metrics that are available before a kernel is executed.

(Andrew Kerr, Gregory Diamos and Sudakhar Yalamanchili: “Modeling GPU-CPU Workloads and Systems”. Proceedings of the Third Workshop on General-Purpose Computation on Graphics Processing Units (GPGPU-3), Pittsburgh, PA. Apr. 2010. PDF Link.)

This symposium is organized by the University of Pittsburgh’s Center for Simulation & Modeling and Pittsburgh Supercomputing Center. The program includes a half-day hands-on tutorial, and several invited talks and presentations by experts from academia and industry. Registration is required. More information can be found at the symposium website.

Yellow Dog Enterprise Linux for CUDA (YDEL for CUDA) is an open source, Linux operating system built for faster, easier, and more reliable GPU Computing. YDEL for CUDA, released and supported by Fixstars, goes beyond the basic Linux OS and integrates support for GPUs, NVIDIA CUDA, and GPU development tools.

From the YDEL for CUDA website:

Key benefits of Yellow Dog Enterprise Linux for CUDA:

• YDEL for CUDA users can experience up to a 9% performance improvement in some applications.
• Comprehensive support is offered to paid subscriptions with our skilled team able to assist you with both Linux and CUDA.
• YDEL’s unparalleled integrations means everything you need to write and run CUDA applications is included and configured.
• YDEL includes multiple versions of CUDA and can easily switch between them via a setting in a configuration file or an environment variable.
• Never worry about updates affecting your system, Fixstars offers YDEL users greater reliability with our strenuous test procedures that validate GPU computing functionality and performance.

For more information, visit the YDEL for CUDA website.